应用于室内家禽养殖环境的复合视觉-激光导航方法
The Composite Visual-Laser Navigation Method Applied in Indoor Poultry Farming Environments
AI总结：
针对室内家禽养殖场中单一传感器导航易受强光和积水干扰的问题，本文提出一种融合激光与视觉的复合导航方法，通过实时可靠性动态计算偏航角，无需物理导航线，实验证明其显著提升了巡检机器人的导航精度与效率。
摘要：
室内家禽养殖场需要巡检机器人来维持精确的环境控制，这对于防止疾病快速传播和大规模禽类死亡至关重要。然而，这些设施内部的复杂条件——以强光照区域和积水区域为特征——带来了显著挑战。依赖单一传感器的传统导航方法在此类环境中往往表现不佳，导致激光漂移和视觉导航线提取不准确等问题。为克服这些局限性，我们提出了一种融合激光与视觉技术的新型复合导航方法。该方法根据每种传感器模态的实时可靠性动态计算融合偏航角，从而无需物理导航线。在实际禽舍环境中的实验验证表明，我们的方法不仅解决了单传感器系统的固有缺陷，而且显著提升了导航精度和运行效率。因此，它为改善复杂室内家禽养殖环境中巡检机器人的性能提供了一种有前景的解决方案。
英文摘要：
Indoor poultry farms require inspection robots to maintain precise environmental control, which is crucial for preventing the rapid spread of disease and large-scale bird mortality. However, the complex conditions within these facilities, characterized by areas of intense illumination and water accumulation, pose significant challenges. Traditional navigation methods that rely on a single sensor often perform poorly in such environments, resulting in issues like laser drift and inaccuracies in visual navigation line extraction. To overcome these limitations, we propose a novel composite navigation method that integrates both laser and vision technologies. This approach dynamically computes a fused yaw angle based on the real-time reliability of each sensor modality, thereby eliminating the need for physical navigation lines. Experimental validation in actual poultry house environments demonstrates that our method not only resolves the inherent drawbacks of single-sensor systems, but also significantly enhances navigation precision and operational efficiency. As such, it presents a promising solution for improving the performance of inspection robots in complex indoor poultry farming settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
